In IoT-based systems, languages like the Uniform Description Language (UDL) have been used to formally describe the features and functionalities of IoT objects. With the integration of social networking concepts into IoT and the emergence of the Social Internet of Things (SIoT), there is now a growing need to specify how objects can become social. Currently, setting up an object’s initial social profile consists only of specifying which social relationships to establish and is often done through predefined rules, which limits both automation and scalability. To address this challenge, we introduce a Uniform Description Language for Social IoT Objects (UDL-SIoT) to support the integration of IoT devices into SIoT platforms. The UDLSIoT provides a structured way to specify the characteristics of IoT devices, including functional, technical, social, quality of service, and security aspects, and use them to create their social profiles. Additionally, we propose to automate the social profile generation by integrating a Deep Learning (DL)-based social profile generator. This generator creates a social profile for objects, that join the network for the first time, based on their features and predicts potential social connections. We evaluate the proposed DL-based social relationship prediction model using a real SIoT dataset. The results show that our generator can predict the social relationships with an accuracy of 92%. The predicted relationships can support advanced tasks such as automating the specification of socialization rules to enable IoT objects to autonomously establish social relationships.

The growing adoption of Internet of Things (IoT) devices has significantly expanded the attack surface of modern networks, underscoring the need for robust anomaly detection solutions. While machine learning-based intrusion detection systems (IDS) have been widely explored, most existing approaches rely on rigid, monolithic pipelines that hinder reuse, reproducibility, and generalization across heterogeneous datasets. To address these challenges, we propose a modular framework for IoT anomaly detection that integrates automation, configurability, and explainability within a unified pipeline. Each stage of the workflow (exploratory data analysis, preprocessing, feature selection, class balancing, model training, evaluation, and SHAP-based explainability) is encapsulated into independent modules controlled by a central configuration block, enabling the automatic generation of detection models and seamless adaptation without code modifications. Extensive experiments on the CIC-IoT2023 dataset, across fine-grained, grouped, and binary classification settings, led to the identification of an optimal configuration. Cross-dataset validation on CIC-Bot-IoT confirmed the effectiveness of this configuration, demonstrating the robustness of the framework and its ability to generate wellperforming models across heterogeneous IoT environments. Beyond this, the framework provides a unified environment for the systematic comparison of anomaly detection methods, supporting reproducibility, extensibility, and practical deployment.

The growth of connected vehicles and their associated services has endowed them with the remarkable ability to rapidly generate vast volumes of data. This proliferation has led to an increasing demand for effective data governance solutions. This paper delves into the exploration of currently available in-vehicle data, meticulously assessing the aspects of data velocity and heterogeneity. By scrutinising these factors, the paper aims to pinpoint and address critical gaps in how to deal with in-vehicle data, ultimately striving to create a seamless platform for managing and harnessing in-vehicle data. This project explores approaches for various connected vehicle communications, including V2V, V2I, and V2X, to de?ne data feeds in the connected vehicle data landscape. The results of the study could in uence the design of in-vehicle data governance by providing information on a stronger integrated framework, helping data owners and users make informed decisions about managing their data assets.

Despite the significant advances achieved in deep learning, the deep neural networks' (DNNs) design approach remains notoriously tedious, depending primarily on intuition, experience, and trial and error. This human-dependent process is often time-consuming and prone to errors. Furthermore, the models are generally bound to their training contexts, with no considerations to their surrounding environments. Continual adaptiveness and automation of neural networks is of paramount importance to several domains where model accessibility is limited after deployment (e.g., IoT devices, self-driving vehicles, etc.). Additionally, even accessible models require frequent maintenance postdeployment to overcome issues such as data/concept drift, which can be cumbersome and restrictive. By leveraging and combining approaches from neural architecture search (NAS) and continual learning (CL), more robust and adaptive agents can be developed. This study conducts the first extensive review on the intersection between NAS and CL, formalizing the prospective paradigm and outlining research directions for lifelong autonomous DNNs.

Today’s cellular networks (known as 2G, 3G, and 4G) provide a solid foundation for connecting things. The Internet of Things (IoT) is helping people live and work smarter and take full control of their lives. In addition to providing smart devices for home automation, IoT is also critical for businesses. The security of classical cryptosystems is characterized by their simple arithmetic complexity. This type uses; shifts, arrangements, permutations, and substitutions of letters, words, or phrases to encrypt a given message. Moreover, this kind of cryptosystem is easy to break, because the complexity of their scheme is convergent. On the other hand, the security of modern cryptosystems is characterized by complex arithmetic, using the concept of keys; private keys to encrypt and public keys to decrypt. This type is hard to break because of the complexity of their scheme being divergent. This paper aims to secure the 5G networks with a new variant of the classical Polybius Checkerboard Cipher (HPCC). The system of letter substitution is based on the complexity and the divergence of private key generation. Filling a magic square with primes is an NP-hard task, which shows the complexity and divergence of the strategy adopted in this variant.

In Business Intelligence (BI) systems, decision-making is based on data warehouses (DW), alimented, generally, from internal sources to the organization. Decision-making based solely on this data, sometimes, gives a partial and limited view of certain activities. Consequently, the decision maker finds himself constrained to search additional information on the Web in order to understand the external environment. This information can be found in external data sources as the Open Data (OD), to complete a decisional analysis. Therefore, it is valuable to enrich DWs with external data sources. In this context, we propose, in this paper, an approach that aims to explore the Web in order to search and select appropriate OD sources for DW enrichment. This approach is carried out on the basis of Natural Language Processing (NLP) techniques as well as a scrapping process related to the Google Dataset Search engine, in order to provide an efficient solution for making decisions to validate this approach, a tool called "Open Data Search" is developed and the obtained results are presented.

Over the last decade, the demand for greater security in 5G networks has grown significantly. Ensuring data security during transmission against external attacks has become a critical priority. However, existing security systems, which focus on attack identification, face limitations in terms of both security and performance. This requires the implementation of more rigorous measures. Meeting the need for improved security in 5G networks calls for advanced machine learning techniques. To tackle this challenge, a proposed hybrid mechanism employs various machine learning approaches to effectively classify threats, such as denial of service, detection denial, and resource misuse. The incorporation of the DET model improves the accuracy of decision making and improves attack classification for 5G networks. Key accuracy parameters, including recall, precision, and F-score, play a crucial role in ensuring the model’s reliability. Simulation results demonstrate the superiority of the proposed model compared to others, particularly in terms of accuracy. Our approach presents a promising solution for identifying and categorizing attacks in 5G networks. By prioritizing accuracy and providing superior performance, this research significantly contributes to ongoing efforts to improve 5G network security.

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

Textual medical knowledge for the public health sector exists as data lakes and repositories across diverse distributed computing systems. However, access to these is impacted by diverse challenges including the need for data aggregation, lack of appropriate contextual basis for data and challenges with scaling the data for widespread availability and accessibility in usable formats. Hence, this research investigates a model for organizing and integrating medical information into semantic knowledge graphs. This is based on a design patterns and semantic standards methodology to facilitate modular, yet cohesive system components for designing and developing semantic knowledge graphs for scalable knowledge discovery and acquisition in the public health sector. Based on these, a conceptual framework for achieving the proposed solution is developed, incorporating design patterns best practices with semantic standards and technologies such as knowledge modelling and graph schemas. Adoption of the framework for practical implementation of public health knowledge portals presents a means of addressing the challenges and fostering improved state of public healthcare systems.

Microservices auto-scaling is an attracking research domain focusing on the dynamic definition of strategies to optimize system efficiency and performance while minimizing service cost. An efficient auto-scaling strikes a balance between individual microservices quality requirements while collaborating to uphold the overall system quality. Maintaining such balance needs a formal method to establish relevant parameters to ensure quality attributes and enable prior accuracy and efficiency verification. To that end, we extend the Petri net model to define Hierarchical Parallel Petri nets (HPPNs) to correctly model both autonomy and cooperation of microservices. Unlike conventional Petri nets, HPPNs consider trade-offs between tokens during transitions, enabling the computation of compromises between multiple quality dimensions and the adaptation strategies. Additionally, preliminary testing mechanisms define the qualities of individual components, providing a solid foundation for assessing microservices’ performance and quality attributes.

Cyber-Physical-Social System (CPSS) big data, the term that has been popularized over the past decade, is different from other types of big data because and is often used to refer to data sets that are too large or complex to be analyzed by traditional means. CPSS big data is specified as global historical and local real-time data. Due to the vast and heterogeneous nature of CPSS, this big data requires authentication control and security protocols. Blockchain technology has emerged as a promising solution for building secure and decentralized access control frameworks that facilitate data sharing and collaboration in CPSS. However, existing blockchain-based authentication control frameworks have scalability, privacy, and usability limitations. In this context, we proposed a secure authentication technique for CPSS that provide security to the system. Our proposed approach is lightweight and secure against different type of cyber attacks, such as replay attacks, and session hijacking attacks.

The Internet of Things (IoT) is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT context is a non-trivial task due to the low capability of these connected devices. Generally, the IoT device management is based on a centralized entity that validates communication and connection rights. Therefore, this centralized entity can be considered as a single point of failure. Yet, in the case of distributed approaches, it is difficult to delegate the right validation to IoT devices themselves in untrustworthy IoT environments. Fortunately, the blockchain may provide decentralization of overcoming the trust problem while designing a privacy-preserving system. To this end, we propose a novel privacy-preserving IoT device management framework based on the blockchain technology. In the proposed system, the IoT devices are controlled by several smart contracts that validate the connection rights according to the privacy permission settings predefined by the data owners and the stored record array of detected misbehavior of each IoT device. In fact, smart contracts can immediately detect the devices that have vulnerabilities and have been hacked or pose a threat to the IoT network. Therefore, the data owner’s privacy is preserved by enforcing the control over the own devices. For validation purposes, we deploy the proposed solution on a private Ethereum blockchain and give the performance evaluation.

Context-based fake news detection provides means to define and describe a social context for news objects on social media, thereby facilitating detection of fake news through data analysis and patterns recognition. However, while content-based fake news detection has gained popularity with machine learning and NLP techniques, the context-based approach has seen very little exploitation. Therefore, it has become pertinent to significantly explore and integrate other technologies for context-based detection of fake news on social media. With semantic technologies capabilities to provide context-awareness for data, this paper analyses social media context and develops a taxonomy for entities classification. Furthermore, a semantic model is developed to describe classes extracted from the taxonomy towards fully semantically describing concepts, relations, instances, and axioms. The model would enhance fake news detection through semantic annotation for contextual features of news objects and datasets, providing a basis for patterns recognition, analysis, and identification of news articles on social media as either fake or not.

Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for these challenges. To achieve this, a set of requirements towards holistic semantic annotation on the web is defined and mapped with cloud computing mechanisms to facilitate them. Technical specification for the requirements is critically reviewed and examined against each of the cloud computing mechanisms, in relation to their technical functionalities. Hence, a mapping is established if the cloud computing mechanism's functionalities proffer a solution for implementation of a requirement's technical specification. The result is a cloud computing capability model for holistic semantic annotation which presents an approach towards delivering large-scale semantic annotation on the web via a cloud platform.

Pneumothorax, also called a collapsed lung, is the presence of the air outside of the lung in the space between the lung and chest wall. It is generally diagnosed using a chest X-ray. However, for some cases, the diagnosis can be difficult as other medical conditions appear similarly. Machine Learning algorithms have been providing great assistance in detecting and locating pneumothorax lately. In this paper, we propose a 2-Stage Training system to segment images with pneumothorax. This system has been built based on U-Net, the state-of-the-art Fully Convolutional Network (FCN) architecture, with a backbone Residual Networks (ResNet-34) that is pre-trained on the ImageNet dataset. In the beginning, we train the network at a lower resolution. Then, we load the trained model weights to retrain the network with a higher resolution. Moreover, we utilize different techniques including Stochastic Weight Averaging (SWA), data augmentation, and Test-Time Augmentation (TTA). We use the chest X-ray dataset that is provided by the 2019 SIIM-ACR Pneumothorax Segmentation Challenge, which contains 12047 training images and 3205 testing images. Our experiments show that 2-Stage Training leads to better and faster network convergence. Our method achieves 0.8356 mean Dice coefficient placing it among the top 9% of competitors with a rank of 124 out of 1475.

Summary With mobile computing being the number one user paradigm of choice, and cloud computing becoming the chosen supporting infrastructure, the aggregation of these two areas is inevitable. Yet due to their novelty, they suffer from both inherited and new issues. Social computing theory can apply well to this fusion, such as pooling together in a social model to create ad hoc mobile clouds or by providing a greater functionality for mobile devices through resource augmentation from an external cloud. However, due to the highly contested resources within this setting, these resources must be negotiated within a social context. This paper argues that the application of social models to mobile cloud computing can allow mobile devices to employ cooperative strategies for resource sharing to allow aspects such as energy and costs to be minimised. Social computing is the means of using computing resources to augment human intelligence, mobiles can provide these enhancements to social intelligences but in a peer‐to‐peer manner. This paper proposes the design of a novel system which employs aggregated user and application resource profiling, in order to determine the most optimal place to process data, locally or on a remote cloud. Negotiation with the local cloud will then find a balance between optimum energy and resource utilisation.

Summary Image segmentation plays a crucial role in many image processing and understanding applications. Despite the huge number of proposed image segmentation techniques, accurate segmentation remains a significant challenge in image analysis. This article investigates the viability of using echo state network (ESN), a biologically inspired recurrent neural network, as features extractor for efficient color image segmentation. First, an ensemble of initial pixel features is extracted from the original images and injected into the ESN reservoir. Second, the internal activations of the reservoir neurons are used as new pixel features. Third, the new features are classified using a feed forward neural network as a readout layer for the ESN. The quality of the pixel features produced by the ESN is evaluated through extensive series of experiments conducted on real world image datasets. The optimal operating range of different ESN setup parameters for producing competitive quality features is identified. The performance of the proposed ESN‐based framework is also evaluated on a domain‐specific application, namely, blood vessel segmentation in retinal images where experiments are conducted on the widely used digital retinal images for vessel extraction (DRIVE) dataset. The obtained results demonstrate that the proposed method outperforms state‐of‐the‐art general segmentation techniques in terms of performance with an F‐score of 0.92 ± 0.003 on the segmentation evaluation dataset. In addition, the proposed method achieves a comparable segmentation accuracy (0.9470) comparing with reported techniques of segmentation of blood vessels in images of retina and outperform them in terms of processing time. The average time required by our technique to segment one retinal image from DRIVE dataset is 8 seconds. Furthermore, empirically derived guidelines are proposed for adequately setting the ESN parameters for effective color image segmentation.

The tremendous growth of interconnected things/devices in the whole world advances to the new paradigm i.e. Internet of Things (IoT). This special session focuses on the recent challenges, design and issues for Energy Management, Protocols and Security for the next generation networks and IoT. The aim of this special issue in IEEE Transactions on Industrial Informatics is to bring together global ICT state of the art and research trends with new developments in this area. This theme is expected to provide the primary and major resources necessary for researchers, academicians, IT industries, and scientists to adopt and implement new inventions. Ten papers have been selected for publication in this part of the section, following several rounds of rigorous reviews. These papers cover the range of encouraging milestones: energy management, resource management, blockchain, cryptography and security aspects for satisfying demands, new algorithms and protocols for the IoT, cloud computing, fog-computing, smart grid, wireless data center and next-generation networks.

Although the field of usability evaluation is a well-established discipline, there are no studies on how the usability of lexicographic e-services can be evaluated. This includes, for examples efficiency, effectiveness and user satisfaction when looking up for synonyms, meanings, or translations using online lexicons. In this paper, we propose to combine two types of usability evaluations to assess the usability of such services: a subjective user-experience evaluation and a more objective controlled experiment - demonstrating how both methods complement each other. We applied our proposed approach to evaluate two important online lexicographic e-services: a lexicographic search engine developed at Birzeit University (https://ontology.birzeit.edu) as well as Google Translate. The user-experience evaluation was conducted through a survey that involved 622 users, and was designed to measure effectiveness, efficiency, satisfaction and learnability. The controlled experiment involved a set of defined tasks, which were carried out by four teams (12 people) in two laboratories, and their performance was monitored. The tasks were designed to measure effectiveness and efficiency.

The papers in this special section focus on mobile cloud computing. The papers address variety of interesting topics covering different aspects of the Mobile Cloud, such as process offloading, work sharing, performance enhancement of Mobile Clouds, security issues in Mobile Clouds, and applications of Mobile Clouds.

With the pervasive introduction of Internet of Everything (IoE) technologies, use cases are frequently being found operating within harsh environmental conditions. This decreases the need for solutions that permit service delivery to operate in a highly resilient manner. This paper presents an architecture for a novel cloud platform designed for resilient service delivery. It supports networks where poor communication links or high node failure will cause services to be delivered in an nonresilient manner. This could be the result of factors such as high node mobility, poor environmental conditions, and unreliable infrastructure from environment disaster or cyber-attack. This biologically inspired architecture uses a purely distributed multiagent approach to provide self-healing and self-organizing properties, modeled on the characteristics of embryonic development and biological cell communication. To permit high levels of a node churn, this multiagent approach uses local-only communication. Probabilistic cellular automata are used to simulate this architecture and evaluate the efficacy of this approach.

the aim of this paper is to collects, document, examine and critically analyze the current research literature on mobile learning (m-learning) in higher education institutes HEIs in the Kingdom of Saudi Arabia (KSA) published between 2010 and 2017. It explores the acceptance of using the m-learning, the factors that influence the deployment of m-learning. Investigate the trends in m-learning by systematically analyzing the previous studies. explores new emerging practices relating to the use of mobile technologies in nursing education; identify gaps in the research literature of the m-learning. The result shows there is reasonable evidence that the HEIs in Saudi Arabia face considerable factors in implementing m-learning. Also shows that significant studies assessing the effectiveness of m-learning within Saudi Arabia HEIs are lacking and existing studies lacked a theoretical framework. The absence of studies reporting on existing m-learning study reflects the limited penetration of this technology and associated pedagogies and a need to strengthen research in the field of m-learning in the KSA.

Semantic Web Technologies have been an active research area for some time and they are concerned with the development of technological concepts and artefacts that can drive the much elusive semantic web. The idea of a semantic web is a web which comprises of data with well-defined meaning. It is also a web that is context-aware in nature, whereby web documents are easily understandable and able to be processed by machines based on the underlying meaning provided for the documents by making use of annotation data (i.e. metadata). While several concepts have been proposed to drive the semantic web, none has so far demonstrated potentials to transform the current Web 2.0 to a truly semantic Web 3.0. With the advent of diverse technological innovations such as internet of things, cloud computing, big data analytics, etc. it is pertinent to review the state-of-the-art for semantic annotation and how it can be impacted by any of these technologies. This paper provides a review of semantic annotation state-of-the-art and how cloud computing as a paradigm can impact on it.

Digital transformation in Saudi Arabia is one of the core elements to achieve the goals of Saudi Vision 2030. In this paper, we argue that data governance plays a vital role for the success of this vision. This role is further emphasized when considering the country's appetite for emerging technologies such as cloud computing solutions. A thorough survey of literature shows that data governance in general and for cloud computing, more specifically, is under researched. This paper puts the Kingdom of Saudi Arabia Vision 2030 for digital transformation under a test. Supported by an empirical study, this paper provides an early warning of a highly likely failure if data governance is not integrated in the vision as a driver for digital transformation. An empirical survey, using a self-administered questionnaire, is conducted to explore and evaluate the status of data governance in the Saudi Arabia. The results of the study reveal that despite the wide recognition of the importance of data governance, especially for cloud computing, there is a consensus that this area is really in its infancy and almost absent from the Saudi organizations.

Recently, Ambient Intelligence Systems (AmI) in particular Ambient Assisted Living (AAL) are attracting intensive research due to a large variety of application scenarios and an urgent need for elderly in-home assistance. AAL is an emerging multi-disciplinary paradigm aiming at exploiting information and communication technologies in personal healthcare and telehealth systems for countering the effects of growing elderly population. AAL systems are developed to help elderly people living independently by monitoring their health status and providing caregivers with useful information. However, strong contributions are yet to be made on context binding of newly discovered sensors for providing dynamic or/and adaptive UI for caregivers, as the existing solutions (including framework, systems and platforms) are mainly focused on checking user operation history, browser history and applications that are most used by a user for prediction and display of the applications to an individual user. The aim of this paper is to propose a framework for making the adaptive UI from context information (real-time and historical data) that is collected from caregivers (primary user) and elderly people (secondary user). The collected data is processed to produce the contextual information in order to provide assistive services to each individual caregiver. To achieve this, the proposed framework collects the data and it uses a set of techniques (including system learning, decision making) and approaches (including ontology, user profiling) to integrate assistive services at runtime and enable their bindings to specific caregivers, in so doing improving the adaptability parameter of UI for the AAL.

The Cloud Computing technology is evolving constantly but essence remains the same that is to offer distinct cost saving opportunities by consolidating and restructuring information technology as a service. With the continuously increasing cloud provisions, cloud consumers start to have difficulties to find the best relevant services that suit their requirements. Therefore, selecting best services by cloud users is becoming a greater challenge. In this paper, we present a framework of services’ recommendation system in a Cloud environment, using automatic learning techniques. The system aims at finding the services that suit the interests and preferences of cloud consumers by combining content based and behaviour based recommendations. In this paper, we present, USTHBCLOUD, a cloud services recommendation prototype evaluated with an experimental study.

Organizations who have transitioned their development environments to the Cloud have started realizing benefits such as: cost reduction in hardware; relatively accelerated development process via reduction of time and effort to set up development and testing environments; unified management; service and functionality expansion; on-demand provisioning and access to resources and development environments. These benefits represent only a fraction of the full potential that could be achieved via leveraging Cloud Computing for the collaborative software development process. Related efforts in this area have been mainly in the areas of: asynchronous collaboration; collaboration in isolated aspects of the Software Development process, such as coding activities; use of open-source tools for contributing, improving, and managing code, etcetera. Although these efforts represent valid contributions and important enablers, they are still missing important aspects which enable a more holistic process, with solid theoretical foundation. This paper reviews this research area, in order to better assess factors and gaps creating the need to enhance the collaborative software development process in the Cloud, to better meet the pressure to collaboratively create better cloud-agnostic applications.

As cloud computing has become the most popular computing platform, and cloud-based applications a commonplace, the methods and mechanisms used to ensure their survivability is increasingly becoming paramount. One of the prevalent trends in recent times is a turn to nature for inspiration in developing and supporting highly survivable environments. This paper aims to address the problems of survivability in cloud environments through inspiration from nature. In particular, the community metaphor in nature's predator-prey systems where autonomous individuals' local decisions focus on ensuring the global survival of the community. Thus, we develop analogies for survivability in cloud computing based on a range of mechanisms which we view as key determinants of prey's survival against predation. For this purpose we investigate some predator-prey systems that will form the basis for our analogical designs. Furthermore, due to a lack of a standardized definition of survivability, we propose a unified definition for survivability, which emphasizes as imperative, a high level of proactiveness to thwart black swan events, as well as high capacity to respond to insecurity in a timely and appropriate manner, inspired by prey's avoidance and anti-predation approaches.

The development of resilient distributed systems is seen as essential to maintaining stable business and state-run processes due to information systems now underpinning most aspects of society. Cloud computing is now one of the most pervasive usage paradigms and due its novelty, research surrounding its resilience is largely lacking and often varied in terms of developed solutions. Therefore this paper provides an up-to-date review of resilience work in cloud computing. This includes methods of measuring and evaluating resilience, solutions for enabling resilience and alternative architectures developed with a focus upon ensuring resilience from the ground up. Firstly, resilience is defined within the context of cloud computing in order to categorise the work appropriately.

Although federated cloud computing has emerged as a promising paradigm, autonomous orchestration of resource utilization within the federation is still required to be balanced on the basis of workload assignment at a given time. Such potential imbalance of workload allocation as well as resource utilization may lead to a negative cloudburst within the federation. The analytical models found in the literature do not provide explicit framework to provide dynamic measure of workload requirement within a cloud federation environment. An additional challenge is the adoption of operational restrictions from regulatory body, the federation, or the federation participants. The analytical models presented in this paper have addressed workload balancing within a federated cloud environment under the access control restrictions agreed between federation members. The proposed analytical models provide a closed form solution for access probability and resource utilization at a given time. The analytical results are evaluated at different degree of security within the cloud federation environment and efficiency of the proposed workload balancing models is demonstrated. The proposed models can be used for cloud services dimensioning to handle high computational demand.

Cloud computing (CC) is a computing model that enables its customers to access a shared pool of resources (e.g., storage, network, servers, etc.) through the Internet with a pay-per-use pricing model. Different service models are employed in CC including the Platform-as-a-Service (PaaS) model, in which the costumers request a certain set of resources and the cloud service providers provide these resources in the form of a virtual machine (VM) running on one of the thousands of hosting servers or physical machines (PMs) of a data center. Where to "place" VMs, how to "execute" them and whether there is a need to "move/migrate" them are important decisions that affect the overall resource utilization and power consumption in the hosting data center. VM consolidation is a technique of migrating or consolidating VMs to PMs in order to prevent the PMs from being overloaded or reduce the number of active PMs and increase their utilization. Consolidation techniques measure PM utilization to decide whether to consolidate the VMs running on it or migrate some of them to another PM. This study aims to optimize resource utilization and energy efficiency in cloud data centers by proposing a new Logistic Regression based host overloading prediction technique that can be used by any VM consolidation technique. The new algorithm have been evaluated using a dynamic workload using the CloudSim simulator. The simulation results show that the proposed algorithm outperforms all other known host status prediction techniques.

Mobile Edge Computing (MEC) enables ubiquitous and efficient cloud services to mobile users which facilitates mobile cloud computing (MCC) more easily by providing storage and processing capacity within the access range of the mobile devices. To achieve the goals of MEC, Mobile Edge (ME) servers are co-placed with the mobile network base station (at the edge of the mobile network). This eliminates the need to move computation and storage intensive tasks from a mobile device to a centralized cloud server. This in turn reduces the network communication load and delay and it also enhances the quality of service provided for the mobile end users. Applications, such as smart grid applications, content delivery networks, crowed sourcing, traffic management and E-health, will greatly benefit from such deployment. Unfortunately, a large-scale deployment of ME servers that is needed to make hundreds of applications available to millions of users, comes with great management complexity. The emerging technique of Software-Defined Systems (SDSys) abstracts the management complexities of many systems at different layers by utilizing software components. In this paper, we build a software-defined based framework that enable efficient and ubiquitous MCC services by integrating different SDSys components with the MEC system. The integrated framework is implemented and it is evaluated for its feasibility, flexibility and potential superiority.

Big data is a main problem for data mining methods. Fortunately, the rapid advances in affordable high performance computing platforms such as the Graphics Processing Unit (GPU) have helped researchers in reducing the execution time of many algorithms including data mining algorithms. This paper discusses the utilization of the parallelism capabilities of the GPU to improve the the performance of two common clustering algorithms, which are K-Means (KM) and Fuzzy C-Means (FCM) algorithms. Two main parallelism approaches are presented: pure and hybrid. These different versions are tested under different settings including two different GPU-equipped machines (a laptop and a server). The results show excellent improvement gains of the hybrid implementations compared with the pure parallel and sequential ones. On the laptop, the best gains of the hybrid implementations compared with the sequential ones are 11.3X for KM and 10.9X for FCM. As for the server, the best gains are 13.5X for KM and 16.3X for FCM. Moreover, the paper explores the usage of a recent memory management technique for GPU called Unified Memory (UM). The results show a decrease in the performance gain of the hybrid implementations that is equal to 44% for hybrid version of KM and 61% for FCM. On the other hand, the use of UM does introduce a small advantage for the pure parallel implementation.

The convergence of the Internet of Things (IoT) with the cloud has been a subject of research interest. Evidence suggests that such a convergence carries huge potential, albeit with some challenges too. There is a consensus that privacy, security and governance are key concerns. One central issue is the lack of mature governance and security standards for data within IoT-cloud converged environments. This paper will add to the wider discussions of the current phenomenon, and argue that roles, responsibilities and policies are the key pillars for developing robust governance and security processes and procedures. Our contribution details these pillars in the context of IoT-Cloud converged environments, provide a generic framework for data governance and security, and provides a roadmap for future work.

Parallel programming has many benefits that can help developers and researchers to improve the performance of some algorithms to become more efficient in real life. This is especially true for systems involving medical images. Image segmentation for volume extraction is a famous segmentation process that takes long time to finish execution. In this paper, we consider a new version of the Fuzzy C-Means (FCM) segmentation algorithm (known as IT2FPCM) and provide a parallel implementation of it that is 12X time faster than the sequential implementation. The considered algorithm is based on Interval Type-2 FCM and combines fuzzy and possibilistic ideas in order to obtain higher accuracy. We conduct our experiments using two different machines and the results show that the improvement gains for both machines 11X and 12X, respectively.

Cloud computing together with emerging technologies has made dramatic and descriptive changes to the traditional datacenters by enabling cost savings and introducing modern resource optimization technics to move non-critical business functions into pay-as-you-go services. Multi-tenancy is one of the top ranked technology which fuels cloud computing. Multi-tenancy is a technology which allows multiple tenants to share single instance of data and applications. The primary contribution of this paper is two-folds; the first one is the proposal and implementation of a more efficient and secure multi-tenancy architecture, and the second is the development of a novel performance evaluation framework based on benchmarking for the proposed multi-tenancy architecture. Research in the area of multi-tenancy in the cloud, despite its significance, it is still immature and unexploited fully.

Cloud computing provides a convenient and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices. With the growing popularity of cloud computing, researchers in this area need to conduct real experiments in their studies. Setting up and running these experiments in real cloud environments are costly. However, modeling and simulation tools are suitable solutions that often provide good alternatives for emulating cloud computing environments. Several simulation tools have been developed especially for cloud computing. In this paper, we present the most powerful simulation tools in this research area. These include CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud. Also, we perform experiments for some of these tools to show their capabilities.

The diversity, continuous expansion and the large number of smart grid resources increase the complexity of such systems and arise the need to find a new way to manage these resources and, at the same time, reduce the complexity in the control and monitoring operations. Moreover, smart grid Systems are considered critical systems requiring reliable and real time data delivery and an efficient, flexible, scalable control solution to satisfy the system requirements. Thus, in this paper, we introduce a new architecture model for smart grid networks based on the Software Defined Systems (SDSys) paradigm. The main idea behind the SDSys revolves around abstracting the control plane out of the data plane and setting it at a separate layer in the middleware. Different forms of SDSys like Software Defined Network (SDN), Software Defined Storage (SDStore), Software Defined Security (SDSec) and Software Defined Internet of Things (SDIoT) are used to provide a comprehensive smart grid control solution which hide the complexity that exists in traditional control techniques. Meanwhile, we show how the proposed model can provide an accurate, reliable, secure, extensible and network-aware architecture design for smart grid network. Furthermore, we explain how it can provide a single point of view for all smart grid resources and a programmable interface to adapt the network for any sudden changes.

With the rapid growth of data centers and the unprecedented increase in storage demands, the traditional storage control techniques are considered unsuitable to deal with this large volume of data in an efficient manner. The Software Defined Storage (SDStore) comes as a solution for this issue by abstracting the storage control operations from the storage devices and set it inside a centralized controller in the software layer. Building a real SDStore system without any simulation and emulation is considered an expensive solution and may have a lot of risks. Thus, there is a need to simulate such systems before the real-life implementation and deployment. In this paper we present SDStorage, an experimental framework to provide a novel virtualized test bed environment for SDStore systems. The main idea of SDStorage is based on the Mininet Software Defined Network (SDN) Open Flow simulator and is built over of it. The main components of Mininet, which are the host, the switch and the controller, are customized to serve the needs of SDStore simulation environments.

This paper proposes an efficient software based data possession mobile cloud computing framework. The proposed design utilizes the characteristics of two frameworks. The first one is the provable data possession design built for resource-constrained mobile devices and it uses the advantage of trusted computing technology, and the second framework is a lightweight resilient storage outsourcing design for mobile cloud computing systems. Our software based framework utilizes the strength aspects in both mentioned frameworks to gain better performance and security. The evaluation and comparison results showed that our design has better flexibility and efficiency than other related frameworks.

The need and drive for sustainable development have never been greater. This creates demand for radical ways to improve efficiency and resource productivity. This paper describes how integrating the Internet of Things (IoT), Big Data and Cloud computing creates a recipe for driving sustainable development and growth. Issues and challenges associated with IoT technology are explored and a framework for the integration of these three technologies with sustainability strategies is presented. The economic, social and environmental impact of the proposed framework is discussed. to the best of the authors' knowledge, there is no reported research which discusses the IoT from a sustainability point of view.

The design and optimisation of Energy Harvesting (EH) Micro-Electromechanical-Systems (MEMS) is of particular interest in this research. The application of such devices is becoming an attractive alternative to the traditional use of batteries in wireless and body sensor networks. An evolutionary Multi-Objective Design Optimisation (DO) Framework is developed to experiment with one class of EH-MEMS, namely, Piezoelectric, using a reconstructed analytical model of the system. The application of such a Framework in this application domain is unprecedented and has already shown very promising results and in some cases it outperformed the human engineer. A thorough analysis of the results has been undertaken, which reveals interesting conclusions about the behaviour and physics of such devices. Besides, the main features of the Framework are explored enabling the enhancement of the MEMS-DO.

This paper shows a successful application of evolutionary algorithms for the design and optimisation of complex real world digital circuit that is a 32-Step Traffic Lights Controller. It discusses two important features of electronic design through evolutionary processes; creativity and innovation. Results are compared to conventional design topologies; and attempt to analyse the evolved designs is presented.

This paper discusses two important features of electronic design through evolutionary processes; creativity and innovation. Hence, conventional design methodologies are discussed and compared with their counterparts via evolutionary processes. An evolutionary search is used as an engine for discovering new designs for a real world application. Attempts to extract some useful principles from the evolved designs are presented and results are compared to conventional design topologies for the same problems.

Embryonic hardware systems satisfy the fundamental characteristics found in nature which contribute to the development of any multi-cellular living being. Attempts of researchers' in this field to learn from nature have yielded promising results; they proved the feasibility of applying nature-like mechanisms to the world of digital electronics with self-diagnostic and self-healing characteristics, Design by humans however often results in very complex hardware architectures, requiring a large amount of manpower and computational resources. A wider objective is to find novel solutions to design such complex architectures for Embryonic Systems, by problem decomposition and unique design methodologies so that system functionality and performance will not be compromised. Design automation using reconfigurable hardware and EA (evolutionary algorithm), such as GA (genetic algorithms), is one way to tackle this issue. This concept applies the notion of EHW (evolvable hardware) to the problem domain. Unlocking the power of EHW for both novel design solutions and for circuit optimisation has attracted many researchers since the early '90s. The promise of using genetic algorithms through evolvable hardware design will, in this paper, be demonstrated by the authors by evolving a relatively simple combinatorial logic circuit (full-adder).